DEICTIC: A compositional and declarative gesture description based on hidden markov models. Issue 122 (February 2019)

• Record Type: Journal Article
• Authors: Carcangiu, Alessandro; Spano, Lucio Davide; Fumera, Giorgio; Roli, Fabio
• Is Part Of: International journal of human-computer studies. Issue 122(2019)
• Page Start: 113
• Page End: 132
• Publication Date: 2019-02
• Subjects: Gestures -- Classification -- Hidden markov models -- Compositional gesture modelling -- Declarative gesture modelling,; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2018.09.001
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: Declarative composition of HMMs for recognizing stroke gestures. Support for sub-gestures identification and prediction. Supports the implementation of feedback and feedforward with an effert comparable to heurist approaches, together with a definition procedure and accuracy comparable to machine learning approaches. High accuracy in recognising composite gestures training only the primitives. Abstract: The consumer-level devices that track the user's gestures eased the design and the implementation of interactive applications relying on body movements as input. Gesture recognition based on computer vision and machine-learning focus mainly on accuracy and robustness. The resulting classifiers label precisely gestures after their performance, but they do not provide intermediate information during the execution. Human-Computer Interaction research focused instead on providing an easy and effective guidance for performing and discovering interactive gestures. The compositional approaches developed for solving such problem provide information on both the whole gesture and on its sub-parts, but they exploit heuristic techniques that have a low recognition accuracy. In this paper, we introduce DEICTIC, a compositional and declarative description for stroke gestures, which uses basic Hidden Markov Models (HMMs) to recognise meaningful predefined primitives (gesture sub-parts) and it composes them to recognise complex gestures. It provides information for supporting gesture guidance and it reaches an accuracy comparable with state-of-the-art approaches, evaluated on two datasets from the literature. Through a developer evaluation, we show that the implementation of a guidance system with DEICTIC requires an effort comparable to compositional approaches, while the definition procedure and the perceived recognition accuracy is comparable to machine learning.